DNA Microarray Data Clustering by Hidden Markov Models and Bayesian Information Criterion View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2006

AUTHORS

Phasit Charoenkwan , Aompilai Manorat , Jeerayut Chaijaruwanich , Sukon Prasitwattanaseree , Sakarindr Bhumiratana

ABSTRACT

In this study, the microarray data under diauxic shift condition of Saccharomyces Cerevisiae was considered. The objective of this study is to propose another strategy of cluster analysis for gene expression levels under time-series conditions. The continuous hidden markov model was newly proposed to select genes which significantly expressed. Then, new approach of hidden markov model clustering was proposed to include Bayesian information criterion technique which helped to determine the size of model. The result of this technique provided a good quality of clustering from gene expression patterns. More... »

PAGES

827-834

Book

TITLE

Advanced Data Mining and Applications

ISBN

978-3-540-37025-3
978-3-540-37026-0

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/11811305_90

DOI

http://dx.doi.org/10.1007/11811305_90

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1008608538


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